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---
license: bsd-3-clause
metrics:
- accuracy
base_model:
- MIT/ast-finetuned-audioset-10-10-0.4593
pipeline_tag: audio-classification
tags:
- signal
- radio
- rf
- emission
- spectrum
---
# Audio Spectrogram Transformer finetuned on SIGID wiki for Radio Signal Classification
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on radio signal recordings documented in [SIGID wiki](https://www.sigidwiki.com).
It was built by [ATDI](https://www.atdi.com)'s AI team to provide a base model for **radio signal classification**.
It achieves the following results on the evaluation set:
- Accuracy: 0.99
- Validation Loss: 0.05
# Dataset
We built our own dataset using [SIGID wiki](https://www.sigidwiki.com). The model is trained to recognize the following radio systems:
- 4G LTE Network
- 5G "New Radio" cellular network - Downlink
- Aircraft Communications Addressing and Reporting System (ACARS)
- Amplitude Modulation (AM)
- Automatic Identification System (AIS)
- Automatic Link Set-up (ALIS)
- Automatic Picture Transmission (APT)
- Bluetooth
- Differential Global Positioning System (DGPS)
- Digital Audio Broadcasting Plus (DAB+)
- Digital Mobile Radio (DMR)
- Digital Video Broadcasting — Terrestrial (DVB-T)
- High Frequency Data Link (HFDL)
- Instrument Landing System
- M20 Radiosonde
- Morse Code (CW)
- Non-Directional Beacon (NDB)
- Radar altimeter
- STANAG 5065
- Secondary surveillance radar (SSR)
- Single Sideband Voice
- Tetrapol
- VHF Data Link - Mode 2 (VDL-M2)
- VHF Omnidirectional Range (VOR)
# Usage
You can use the raw model for classifying signals into one of the SIGID wiki classes specified above and in config.json.
You can also fine-tune this model on your own radio signal dataset to make it more specific.